class_docstr: The LogisticRidgeRegression class is used to fit logistic ridge regression models against one or more labels optimized over a
  provided list of ridge alpha parameters. The optimal ridge alpha value is chosen for each label by minimizing the
  average out of fold log_loss scores.
code: def _unpersist_model_cv_df(self) -> None:
      _check_model(self.model_df)
      _check_cv(self.cv_df)
      self.model_df.unpersist()
      self.cv_df.unpersist()
  
imports: from .logistic_udfs import *
  from .ridge_reduction import RidgeReduction
  from .model_functions import _prepare_labels_and_warn, _prepare_covariates, _check_model, _check_cv, _is_binary
  from nptyping import Float, NDArray
  import pandas
  from pyspark.sql import DataFrame
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  import pyspark.sql.functions
  from typeguard import typechecked
  from typing import Any, Dict, List
  import warnings
  from glow.logging import record_hls_event
label_desc: Use image node_id 11 for calling the LogisticRidgeRegression obj's underlying member method code with example usage: obj._unpersist_model_cv_df() without return types